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检索条件"机构=Information Technology and Computer Science Program"

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Digital data security and hiding on virtual reality VDO 3DGIS-Map

Digital data security and hiding on virtual reality VDO 3DGI...

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IEEE International Conference on Management of Innovation and technology, ICMIT

作者： Katanyoo Klubsuwan Surasak Mungsing PHD-Information Technology Sripatum University Bangkok Thailand Computer Science Program Faculty of Informatics Sripatum University Bangkok Thailand

Todaypsilas information technology supports varieties of e-commerce, in particular on-demand services such as news, message, seminar and presentation speech to message, and 3D Video GIS. Each service can have value-added by embedding other hidden-service within the main service, hence promoting value-added to the service. The value-added services are accomplished by using the technique of Multiple Keys and Messages Embedding (MKME), which hidden-contents can be retrieved only by applying the correct corresponding decryption keys This paper presents the design and algorithm for multiple keys and messages embedding on 3D Video GIS, based on Steganography concept. The quality of the resulting product was also investigated. The main data used is geospatial video, which is primarily used by the elderly and disables people so that they can feel the surroundings on their desktop while sitting at home.

关键词： Pixel Three dimensional displays Discrete cosine transforms Geographic information systems Security Image color analysis Cryptography

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Fuzzy Fusion for Affective State Assessment in Distance Learning Based on Image Detection

Fuzzy Fusion for Affective State Assessment in Distance Lear...

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2008 International Conference on Audio，Language and Image Processing(2008国际声音、语言、图像过程大会)

作者： Kuoan Hwang Chiahao Yang Department of Computer Science and Information Engineering & Graduate Institute of Networking and Co Graduate Institute of Informatics Doctoral program Chaoyang University of Technology

Distance learning is a learning style that can overcome the limitation of time and space. Because of the distance, teachers can not handle the student's learning situation, and they do not know whether the student is attentive, drowsy or absent. If teachers can know the student's affective state, they can overcome the difficult. The research applies the image recognition technologies to capture the face images of students when they are learning and analyzes their face features to evaluate the student's affective state by Fuzzy Integral. Finally, teachers can monitor the student's behavior by the detection results on the system interface.

关键词： Distance learning Skin computers Distance measurement Face Image color analysis Education

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Attentiveness Assessment in Learning Based on Fuzzy Logic Analysis

Attentiveness Assessment in Learning Based on Fuzzy Logic An...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Kuo-An Hwang Chia-Hao Yang Department of Computer Science and Information Engineering & Graduate Institute of Networking and CommunicationEngineering ChaoYang University of Technology Taiwan Graduate Institute of Informatics Doctoral program ChaoYang University of Technology Taiwan

Learner attention affects learning efficiency. However, in many classes, teachers cannot assess the degree of attention of every student. When a teacher is capable of addressing inattentive students immediately, he can avoid situations in which students are inattentive. Many studies have analyzed student attentiveness by the applying of image detection technologies. If this mechanism can be applied to in-class learning, it will help teachers keep students attentive, and reduce teacher load during class. This study mainly applies fuzzy logic analysis of student facial images when participating in class. Applying fuzzy logic can prevent erroneous judgments associated with a single term, and help teachers deal with student attentiveness.

关键词： Fuzzy logic Face detection Image analysis Image color analysis Education Eyes Design engineering Chaotic communication Image processing Face recognition

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Lessons learned from adapting a generic narrative diabetic-foot guideline to an institutional decision-support system

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作者： Peleg, Mor Wang, Dongwen Fodor, Adriana Keren, Sagi Karnieli, Eddy Department of Management Information Systems University of Haifa Haifa 31905 Israel Biomedical Informatics Program University of Rochester NY United States Institute of Endocrinology Diabetes and Metabolism Rambam Medical Center Israel Galil Center Technion-Israel Institute of Technology Israel Department of Computer Science University of Haifa Israel

ISBN: (纸本)9781586038731

Clinical guidelines usually need to be adapted to fit local practice before they can be actually used by clinicians. Reasons for adaptation include variations of institution setting such as type of practice and location, availability of resources, differences in patient populations, local policies, and practice patterns. When a guideline is implemented for clinical decision support and integrated with an institution's clinical information system, the data model of the local electronic medical record (EMR) and the data actually collected and stored in it also influence the guideline's adaptation. The purpose of this work is: (1) to characterize a tool-supported process for guideline encoding that addresses local adaptation and EMR integration, and (2) to identify the types of changes in guideline encoding during the local adaptation process. © 2008 The authors and IOS Press. All rights reserved.

关键词： Signal encoding

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Using Bayesian Network for planning course registration model for undergraduate students

Using Bayesian Network for planning course registration mode...

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Inaugural IEEE-IES Digital EcoSystems and Technologies Conference, DEST

作者： Pathom Pumpuang Anongnart Srivihok Prasong Praneetpolgrang Somchai Numprasertchai Department of Computer Science Faculty of Science Kasetsart University Bangkok Thailand Master of Science Program in Information Technology Graduate School Sripatum University Bangkok Thailand Department of Computer Engineering Faculty of Engineer Kasetsart University Bangkok Thailand

ISBN: (纸本)9781424414895

The objective of this study is to propose a model for planning course registration by using a data mining technique: Bayesian network. The proposed model can be used to predict the sequences of courses to be registered by undergraduate students whose majors are computer science or engineering. The data set was obtained from student enrollments and include GPA and grades in each subject for first and second year students from a private university in Thailand. Evaluations show that the predictive power of this model is acceptable. The implications from this studypsilas findings suggest that the model can be applied for advising students in planning courses to be registered in each semester. Further, the model appears to be useful for improving curriculum development in order to fit both studentspsila and university requirements.

关键词： Bayesian methods Biological system modeling computers Education Computational modeling Brain modeling Brain models

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Recommending mobile content using content diffusion and user preference transition

Recommending mobile content using content diffusion and user...

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2008 International Conference on e-Learning, e-Business, Enterprise information Systems, and e-Government, EEE 2008

作者： Lee, Tong Queue Lee, Eunchang Park, Young Park, Yongtae Department of Mobile Internet Dongyang Technical College Seoul Korea Republic of Graduate Program in Technology and Management Seoul National University Seoul Korea Republic of Department of Computer Science and Information Systems Bradley University Peoria IL United States Department of Industrial Engineering Seoul National University Seoul Korea Republic of

ISBN: (纸本)1601320639

Recommendation accuracy is especially important in mobile e-commerce environments due to the limited screen size of mobile devices and relatively expensive connection costs. Mobile content tends to be fashionable and are geared for young users. This paper presents a novel method of building a more accurate recommender system for mobile content in a mobile ecommerce environment. The method is based on collaborative filtering, and models content diffusion and user preference transition and incorporates them in constructing pseudo ratings from implicit feedback data. In a variety of experiments, recommender systems based on the method showed significantly better recommendation accuracy than a pure collaborative filtering-based recommender system.

关键词： Collaborative filtering

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A Security Coordination Model for an Inter-Organizational information Incidents Response Supporting Forensic Process

A Security Coordination Model for an Inter-Organizational In...

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International Conference on Networked Computing and Advanced information Management (NCM)

作者： Kimoon Jeong Junhyung Park Minsoo Kim Bongnam Noh Interdisciplinary Program of Information Security Chonnam National University South Korea Division of Electronics Computer & Information Engineering Chonnam National University South Korea Korea Institute of Science and Technology Information South Korea Department of Information Security Mokpo National University South Korea

The high cost for information security incident response makes organizations hesitate to possess their own expert security team. Also organizations are still reluctant to share their own security circumstances with external organizations. By the way, they hope experts will help to defend against cyber threats without losing their reputations. To satisfy these requests of organizations, we propose a security coordination model that supports security incidents response in an organizational architecture, in this paper. Besides, our model has cyber forensic functions for collecting digital evidences from real time monitoring and on-site investigation of the incidents and reporting the analysis results to authorized organizations effectively.

关键词： Security Organizations Monitoring Real time systems information security computer architecture Law enforcement

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A decision support system for evaluating international student applications

A decision support system for evaluating international stude...

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37th Annual Frontiers in EducationConference

作者： Hien, Nguyen Thi Ngoc Haddawy, Peter Computer Science and Information Management Program Asian Institute of Technology

ISBN: (纸本)9781424410835

In today's transnational admission environment, evaluating applicant qualifications is becoming increasingly challenging. While standardized tests can be helpful, studies have shown that they are rather noisy predictors of performance. Predicting educational outcome is a viable alternative in such heterogeneous environments. Performance prediction models can be built by applying data mining techniques to enrollment data. In this paper we present an approach to using Bayesian networks to predict graduating cumulative Grade Point Average based on applicant background at the time of admission. While such prediction models can be helpful, their recommendations may not be followed by departmental faculty members making admission decisions if they are presented as black boxes. We thus present a novel approach to deriving a case-based retrieval mechanism from the Bayesian network prediction model in such a way that the similarity measure used by the case-based system is consistent with the predictive model. The case-based component retrieves the past student most similar to the applicant being evaluated. The Bayesian network model is evaluated using stratified ten-fold cross validation.

关键词： Bayesian networks case-based retrieval student evaluation

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Deriving financial aid optimization models from admissions data

Deriving financial aid optimization models from admissions d...

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37th ASEE/IEEE Frontiers in Education Conference, FIE

作者： Van Thanh, Le Haddawy, Peter Asian Institute of Technology Computer Science and Information Management Program

ISBN: (纸本)1424410843

This paper presents a novel approach to deriving probabilistic models that predict enrollment given applicant background and the amount of financial aid offered. Our Bayesian network models can be used to optimize various enrollment objectives. We present a novel efficient optimization algorithm that uses the models to maximize expected tuition revenue under capacity constraints including student-faculty ratio and accommodation. We demonstrate and evaluate our approach using four years of graduate admissions data from the Asian Institute of technology, consisting of 7,788 applicants from 84 different countries. This data set is particularly challenging since reliable family income data is not available for students from most of these countries. Evaluating the Bayesian network model with 10-fold cross validation yields an ROC Az value of 0.8451, with a predictive accuracy of 82.70% at a threshold of 0.5. Comparing the results of the tuition revenue optimization model to the institute's current financial aid allocation practice shows that if single-term tuition revenue is the sole optimization criterion, the institute can achieve its current enrollment numbers while realizing significant savings in its financial aid budget. The prediction and optimization software is currently being incorporated into the institute's online admissions processing system. ©2007 IEEE.

关键词： Bayesian networks

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A comparative analysis of techniques for predicting academic performance

A comparative analysis of techniques for predicting academic...

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37th Annual Frontiers in EducationConference

作者： Nghe, Nguyen Thai Janecek, Paul Haddawy, Peter Computer Science and Information Management Program Asian Institute of Technology (AIT) 12120 Thailand AIT Thailand Can Tho University Viet Nam

ISBN: (纸本)9781424410835

This paper compares the accuracy of Decision Tree and Bayesian Network algorithms for predicting the academic performance of undergraduate and postgraduate students at two very different academic institutes: Can Tho University (CTU), a large national university in Viet Nam;and the Asian Institute of technology (AIT), a small international postgraduate institute in Thailand that draws students from 86 different countries. Although the diversity of these two student populations is very different, the data-mining tools were able to achieve similar levels of accuracy for predicting student performance: 73/71% for {fail, fair, good, very good} and 94/93% for {fail, pass} at the CTU/AIT respectively. These predictions are most useful for identifying and assisting failing students at CTU (64% accurate), and for selecting Very Good students for scholarships at the AIT (82% accurate). In this analysis, the Decision Tree was consistently 3-12% more accurate than the Bayesian Network. The results of these case studies give insight into techniques for accurately predicting student performance, compare the accuracy of data mining algorithms, and demonstrate the maturity of open source tools.

关键词： Bayesian Networks data mining Decision Trees prediction

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